Scientific Collaboration Networks on Software

This project investigates how scientific software use—specifically in evolutionary biology—clusters across geographic, linguistic, and academic genealogical lines. Scientific software is not merely a technical tool; it acts as a gatekeeper of community practice and knowledge exchange. By tracing software mentions across 197,000+ scholarly articles (1990–2024), we ask:

• Who uses what software, where, and why?
• Does language or geography affect tool adoption?
• Can these clusters influence scientific progress or hinder researcher mobility?

The core of the study is on phylogenetic software used in building evolutionary trees, such as BEAST, RAxML, MrBayes, TNT, and many others. Despite serving the same scientific purpose, these tools have been adopted unevenly. This project aims to uncover how software choice becomes a social pattern, potentially shaping careers and impacting the innovation pipeline of evolutionary science.

We collected data from the Constellate website, identified articles containing specific software mentions, and filtered the dataset. Using the DOIs, we extracted author details via OpenAlex, including author IDs and country affiliations. We then prepared an edge list combining:

• Author ID
• DOI
• Software used
• Year of publication
• Language of paper

This consolidated dataset formed the basis for all subsequent network analyses.

This interactive network graph presents the collaborative landscape between authors and the phylogenetic software they use, extracted from ~5,000 scientific papers. It offers a visual summary of:

• How researchers are grouped around specific software tools.
• Which software has broad versus niche adoption.
• Cross-tool usage patterns by authors.

📐 Graph Construction

Data Source: author_id_edgelist_pretty_with_term.csv
A pre-processed dataset listing two authors (author1, author2) per paper, software mentions (Term), often multiple per DOI.

Data Filtering: Only papers with documented software mentions are retained, up to 5,000 DOIs.

Node and Edge Logic:

• Nodes: Software tools (large labeled circles); authors (small black nodes, unlabeled for clarity).
• Edges: Undirected edges connect each author to every software tool they used in a paper.

🎨 Visual Encoding

• Node Size: Software nodes are large to highlight importance; author size weighted by frequency.
• Node Color: Software colored via a mixed palette; authors uniformly black.
• Labels: Software names shown; author labels hidden.

📊 Interpretation & Insights

• Highly Central Software Nodes (e.g., MEGA, BEAST, RAxML) indicate broad user bases.
• Dense Local Clusters reflect tight, specialized user communities (e.g., TNT cluster).
• Peripheral Nodes (e.g., DELTA, WinClada, Hennig86) denote legacy or niche use.
• Overlap and Multiplicity: Authors connected to multiple software highlight methodological flexibility.

This bipartite network visualization shows the relationship between publication languages and phylogenetic software tools used in scientific literature.

🔗 Node Connections (Edges)

• Edges link a language node (circle sized equally) to software nodes.
• Edge thickness indicates co-occurrence frequency in that language's papers.

📌 Interpretation & Insights

• Dominant Languages: English and Spanish appear central, linking to nearly all major tools.
• Language-Specific Tools: Some software exclusively linked to certain languages (e.g., Java-based tools with limited languages).
• Multilingual Tools: Software connected to multiple languages indicate broad cross-language support.
• Community Patterns: Clusters reflect shared methodological preferences shaped by language communities.

This dual-panel bar chart visualizes how frequently various phylogenetic software tools are used across different publication languages (relative frequency %).

🔹 Top Panel: Main Chart

• X-axis: Phylogenetic software tools (e.g., NDE, MEGA, RAxML, MrBayes, etc.).
• Y-axis: Relative frequency (%) of software mentions per language.
• Colored Bars: Each bar represents a language (legend). E.g., en→English, es→Spanish, de→German, pt/fr/it/nl, etc.

✳️ Key Observations

• NDE, MEGA, IQ-TREE, PAUP show high usage across most languages, especially English (en) and Spanish (es).
• Welsh (cy), Romanian (ro), and Afrikaans (af) show minimal or niche tool usage.
• Some tools appear almost exclusively in English publications, highlighting the dominance of English in scientific software discussion.

🔍 Bottom Panel: Zoom (≤1% Frequency)

This subplot isolates rarely used tools (≤1% frequency) to reveal niche software such as:

• Mesquite, MacClade, Phylip, DendroPy, Xper2, DNAPARS, Hennig86.

It ensures outlier detection and highlights lesser-known platforms otherwise overshadowed.

This graph visualizes the relationship between phylogenetic software tools and their publication-year usage patterns, styled as a chord-like circular network. It shows how different tools are distributed over time, emphasizing both historical trends and recent popularity.

🧩 Graph Design

🔗 Nodes

• Left Half (Green Nodes): Years 1990–2022 represent publication years.
• Right Half (Colored Nodes): Software tools, each colored distinctly (e.g., NDE=blue, MEGA=orange, MrBayes=red, PAUP=purple).

🔗 Edges

• Connect software nodes to years.
• Edge thickness encodes relative frequency of mentions.

📈 What This Graph Reveals

• Historical Adoption: NDE and MEGA show thick edges dating back to 1990s. PAUP and MrBayes peak early 2000s.
• Emerging Trends: RAxML, TNT, IQ‑TREE gain traction in recent decades.
• Legacy vs. Modern: DELTA and Hennig86 have sparse edges. Recent years (2020–2022) show high diversity of tool usage.

This interactive visualization reveals how phylogenetic software tools are used across different countries, based on mention frequencies in scientific publications. It shows global distribution, regional preferences, and dominant software footprints.

🧩 Network Design

• Nodes:
  
  • Countries: Large sky-blue circles (e.g., US, CN, FR, IN).
  • Software Tools: Medium orange circles (e.g., MEGA, NDE, PAUP).
• Tooltips:
  
  • Countries: “Country: <Code>”
  • Software: “Software: <Name>”

🔗 Edges

Edges connect countries to software based on mention counts. Thicker edges = higher mention frequency.

📊 Insights & Patterns

• Global Adoption: MEGA and NDE are central hubs with thick edges to many countries.
• Regional Preferences: Tools like TNT, PAST, DELTA show strong ties to specific regions.
• Legacy Software: DNAML, MacClade, Hennig86 appear sparsely at the periphery.
• Research Clusters: Interactive layout highlights clusters of countries sharing toolsets.

Our study shows that software scientists use for building evolutionary trees isn't just about features or performance—it’s also shaped by where they live, what language they speak, and who they work with. By analyzing nearly 200,000 scientific articles, we found clear patterns: certain tools like MEGA and NDE are used almost everywhere, while others like TNT or PAUP are more popular in specific countries or communities.

We also saw that English dominates scientific publishing, but tools like PAST, DELTA, and BEAST show up in other languages too—though less often. Some software are only used in niche regions, suggesting that language and geography still influence access and adoption.

Over time, we’ve moved from older parsimony-based tools to newer Bayesian and likelihood-based ones like MrBayes, RAxML, and IQ-TREE. These changes reflect shifts in the field but also in how scientists train and work together.

Looking at how authors and software are connected, it’s clear that people often cluster around specific tools—probably influenced by mentors, departments, or even hiring trends. This kind of clustering can help build strong support networks, but it might also make it harder for new ideas or people to break in.

In short, scientific software is more than just code. It reflects the structure of the scientific community: how ideas spread, how people collaborate, and how careers evolve. To truly support innovation and inclusion, we need to pay attention not just to what tools are used—but who’s using them, where, and why.